Fundamentals of Analytics Workloads on Azure

No, this course isn't meant to prepare you for any particular certification, but it can help as a part of a larger cert prep learning plan. Rather than focus on exam questions or test objectives, this course is focused on getting you a quick and thorough grounding of what analytics workloads are, how Azure helps process and run them, and what your options are in the data analysis world.

The best audience for this course on the fundamentals of analytics workloads on Azure includes data analysts, data scientists and business intelligence professionals, especially if you're trying to leverage Azure for analytics tasks. If your company is researching possible options to explore analytics processing, this course is an excellent way to familiarize yourself with the vocabulary and concepts. It's also a great start to a career in data analysis with Azure.

An analytics workload is a broad and abstract term that refers to all the processing that a computer system does in order to complete an analytical task or set of tasks. Analytics workloads can include different operations related to processing, analyzing and then producing insights from large volumes of data. Azure is Microsoft's public cloud infrastructure and it has many, many different tools, features and platforms for handling analytics workloads.

Azure has a comprehensive suite of data analysis services and tools that expand the range of options available for processing analytics workloads. Azure services like Azure Synapse Analytics, Azure Databricks or Azure Machine learning are used to ingest, process, analyze and visualize data at scale. These Azure services and others provide data integration, transformation, advanced analytics and machine learning capabilities. With them, businesses make better decisions and get better insights.

The biggest upside to using Azure for analytics workloads is simply that Microsoft operates it. Microsoft Azure integrates practically seamlessly with any other Microsoft product or service, from Office 365 to Power BI. Additionally, Azure has long emphasized scalability, meaning analytics workloads of radically different sizes can be processed. Last, Microsoft has invested heavily in advanced analytics services like AI and machine learning, all of which can be leveraged in Azure.